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| -rw-r--r-- | scripts/alternate_sampler_noise_schedules.py | 53 |
1 files changed, 53 insertions, 0 deletions
diff --git a/scripts/alternate_sampler_noise_schedules.py b/scripts/alternate_sampler_noise_schedules.py new file mode 100644 index 00000000..4f3ed8fb --- /dev/null +++ b/scripts/alternate_sampler_noise_schedules.py @@ -0,0 +1,53 @@ +import inspect +from modules.processing import Processed, process_images +import gradio as gr +import modules.scripts as scripts +import k_diffusion.sampling +import torch + + +class Script(scripts.Script): + + def title(self): + return "Alternate Sampler Noise Schedules" + + def ui(self, is_img2img): + noise_scheduler = gr.Dropdown(label="Noise Scheduler", choices=['Default','Karras','Exponential', 'Variance Preserving'], value='Default', type="index") + sched_smin = gr.Slider(value=0.1, label="Sigma min", minimum=0.0, maximum=100.0, step=0.5,) + sched_smax = gr.Slider(value=10.0, label="Sigma max", minimum=0.0, maximum=100.0, step=0.5) + sched_rho = gr.Slider(value=7.0, label="Sigma rho (Karras only)", minimum=7.0, maximum=100.0, step=0.5) + sched_beta_d = gr.Slider(value=19.9, label="Beta distribution (VP only)",minimum=0.0, maximum=40.0, step=0.5) + sched_beta_min = gr.Slider(value=0.1, label="Beta min (VP only)", minimum=0.0, maximum=40.0, step=0.1) + sched_eps_s = gr.Slider(value=0.001, label="Epsilon (VP only)", minimum=0.001, maximum=1.0, step=0.001) + + return [noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s] + + def run(self, p, noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s): + + noise_scheduler_func_name = ['-','get_sigmas_karras','get_sigmas_exponential','get_sigmas_vp'][noise_scheduler] + + base_params = { + "sigma_min":sched_smin, + "sigma_max":sched_smax, + "rho":sched_rho, + "beta_d":sched_beta_d, + "beta_min":sched_beta_min, + "eps_s":sched_eps_s, + "device":"cuda" if torch.cuda.is_available() else "cpu" + } + + if hasattr(k_diffusion.sampling,noise_scheduler_func_name): + + sigma_func = getattr(k_diffusion.sampling,noise_scheduler_func_name) + sigma_func_kwargs = {} + + for k,v in base_params.items(): + if k in inspect.signature(sigma_func).parameters: + sigma_func_kwargs[k] = v + + def substitute_noise_scheduler(n): + return sigma_func(n,**sigma_func_kwargs) + + p.sampler_noise_scheduler_override = substitute_noise_scheduler + + return process_images(p) |